Image Processing Tool Promoting Decision-Making in Liver Surgery of Patients with Chronic Kidney Disease

Preoperative assessment of the liver volume and function of the remnant liver is a mandatory prerequisite before performing major hepatectomy. The aim of this work is to develop and test a software application for evaluation of the residual function of the liver prior to the intervention of the surgeons. For this purpose, a complete software platform consisting of three basic modules: liver volume segmentation, visualization, and virtual cutting, was developed and tested. Liver volume segmentation is based on a patient examination with non-contrast abdominal Computed Tomography (CT). The basis of the segmentation is a multiple seeded region growing algorithm adapted for use with CT images without contrast-enhancement. Virtual tumor resection is performed interactively by outlining the liver region on the CT images. The software application then processes the results to produce a three-dimensional (3D) image of the “resected” region. Finally, 3D rendering module provides possibility for easy and fast interpretation of the segmentation results. The visual outputs are accompanied with quantitative measures that further provide estimation of the residual liver function and based on them the surgeons could make a better decision. The developed system was tested and verified with twenty abdominal CT patient sets consisting of different numbers of tomographic images. Volumes, obtained by manual tracing of two surgeon experts, showed a mean relative difference of 4.5%. The application was used in a study that demonstrates the need and the added value of such a tool in practice and in education.

DNA extraction method selection for agricultural soil using TOPSIS multiple criteria decision-making model

There is an increased interest in the extraction of nucleic acids from various environmental samples since culture-independent molecular techniques contribute to deepen and broaden the understanding of a greater portion of uncultivable microorganisms. Due to difficulties to select the optimum DNA extraction method in view of downstream molecular analyses, this article presents a straightforward mathematical framework for comparing some of the most commonly used methods. Four commercial DNA extraction kits and two physical-chemical methods (bead-beating and freeze-thaw) were compared for the extraction of DNA under several quantitative DNA analysis criteria: yield of extraction, purity of extracted DNA (A260/280 and A260/230 ratios), degradation degree of DNA, easiness of PCR amplification, duration of extraction, and cost per extraction. From a practical point of view, it is unlikely that a single DNA extraction strategy can be optimum for all selected criteria. Hence, a systematic Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) was employed to compare the methods. The PowerSoil? DNA Isolation Kit was systematically defined as the best performing method for extracting DNA from soil samples. More specifically, for soil:manure and soil:manure:biochar mixtures, the PowerSoil?DNA Isolation Kit method performed best, while for neat soil samples its alternative version gained the first rank.

Using Mathematical Models in Decision Making Methodologies to Find Key Nodes in the Noordin Dark Network

A Dark Network is a network that cannot be accessed through tradition means. Once uncovered, to any degree, dark network analysis can be accomplished using the SNA software. The output of SNA software includes many measures and metrics. For each of these measures and metric, the output in ORA additionally provides the ability to obtain a rank ordering of the nodes in terms of these measures. We might use this information in decision making concerning best methods to disrupt or deceive a given dark network. In the Noordin Dark network, different nodes were identified as key nodes based upon the metric used. Our goal in this paper is to use methodologies to identify the key players or nodes in a Dark Network in a similar manner as we previously proposed in social networks. We apply two multi-attribute decision making methods, a hybrid AHP & TOPSIS and an average weighted ranks scheme, to analyze these outputs to find the most influential nodes as a function of the decision makers’ inputs. We compare these methods by illustration using the Noordin Dark Network with seventy-nine nodes. We discuss sensitivity analysis that is applied to the criteria weights in order to measure the change in the ranking of the nodes.

Decision Making in Sustainable Development: Some Methods to Evaluate Energy and Nonrenewable Resources Waste When Using Some Plastics

This paper explores a decision making model for a multidisciplinary problem in nature. This problem considers the role of energy use in sustainable development and the potential sources to increase energy efficiency during its whole life cycle;it also deals with multicriteria decision making of plastic materials used in a day to day basis. Exergy analysis of plastic materials used to the manufacture of disposable polyethylene bags comparing them with other materials that can be used for substitution will be important to take decisions. We are also interested in plastic poly (ethylene Terephthalate or PET) bottles. The calculation of the incoming and outgoing Exergy flows during the production processes are carried out. The Exergy loss considering the sustainability concept, Green House Gases emissions, real energy flows needed to the chain of processes, material balances in the productions chains and value added, are presented as a set of criteria to make decisions of alternative materials including the actual ones. A case study for Mexico’s market will be developed in order to prove the methodology. It offers some interesting data about consumption and production of bags and bottles.

Patients’ Involvement in Decision Making at End of Life—A Systematic Review of Literature

Background: involving patient in end of life decision is important to understand their wishes and preferences to help health care providers in improving the quality of dying and minimizing suffering. Aim: the aim of this review was to provide a detailed examination of the available literature related to patients’ involvement in decision making at end of life. Design: a systematic review following the PRISMA protocol was used, the review protocol was registered on PROSPERO: CRD42019128556. Data sources: we conducted a literature search in two electronic databases “CINAHL and Medline” during March-April 2019. The retrieved articles were included if they were: research reports or literature review;examined patient involvement in end-of-life discussions;full text publications, written in English and published from 2000-2019. Results: a total of (22) articles were included in the review;there was diversity in the purposes and design approach of the retrieved studies. The available literature explored patient’s involvement at end-of-life decision making through;describing current practices;understanding perspectives of end of life discussions;investigating the impact and identifying the barriers and facilitators of patients’ involvement in end of life discussions. Conclusion: involvement in end-of-life discussions improved the recognition of patients’ wishes, improved death experience, and decreased posttraumatic stress, depression, and anxiety among family members. Despite the documented benefits, some barriers against patient’s involvement in end-of-life decisions were recognized;lack of awareness;lack of education, training and experience;concerns about ethical and legal issues;and personal preferences of doctors or nurses were among the most commonly identified barriers.

Fuzzy Set Based Models and Methods of Decision Making and Power Engineering Problems

The results of research into the use of fuzzy set based models and methods of multicriteria decision making for solving power engineering problems are presented. Two general classes of models related to multiobjective (X,M> models) and multiattribute (X,R> models) problems are considered. The analysisX,M> of models is based on the use of the Bellman-Zadeh approach to decision making in a fuzzy environment. Its application conforms to the principle of guaranteed result and provides constructive lines in obtaining harmonious solutions on the basis of analyzing associated maxmin problems. Several techniques based on fuzzy preference modeling are considered for the analysis of?X,R> models. A review of the authors’ results associated with the application of these models and methods for solving diverse types of problems of power system and subsystems planning and operation is presented. The recent results on the use ofX,M> andX,R> models and methods of their analysis for the allocation of reactive power sources in distribution systems and for the prioritization in maintenance planning in distribution systems, respectively, are considered.

Control Method of Effect of Robust Optimization in Multi-Player Multi-Objective Decision-Making

In the real situations of supply chain, there are different parts such as facilities, logistics warehouses and retail stores and they handle common kinds of products. In this research, these situations are focused on as the background of this research. They deal with the common quantities of their products, but due to their different environments, the optimal production quantity of one part can be unacceptable to another part and it may suffer a heavy loss. To avoid that kind of unacceptable situations, the common production quantities should be acceptable to all parts in one supply chain. Therefore, the motivation of this research is the necessity of the method to find the production quantities that make all decision makers acceptable is needed. However, it is difficult to find the production quantities that make all decision makers acceptable. Moreover, their acceptable ranges do not always have common ranges. In the decision making of car design, there are similar situations to this type of decision making. The performance of a car consists of purposes such as fuel efficiency, size and so on. Improving one purpose makes another worse and the relationship between these purposes is tradeoff. In these cases, Suriawase process is applied. This process consists of negotiations and reviews of the requirements of the purposes. In the step of negotiations, the requirements of the purposes are share among all decision makers and the solution that makes them as satisfied as possible. In the step of reviews of the requirements, they are reviewed based on the result of the negotiation if the result is unacceptable to some of decision makers. Therefore, through the iterations of the two steps, the solution that makes all decision makers satisfied is obtained. However, in the previous research, the effects that one decision maker reviews requirements in Suriawase process are quantified, but the mathematical model to modify the ranges of production quantities of all decision makers simultaneously is not shown. Therefore, in this research, based on Suriawase process, the mathematical model of multi-player multi-objective decision making is proposed. The mathematical model of multi-player multi-objective decision making by using linear physical programming (LPP) and robust optimization (RO) in the previous research is the basis of the methods of this research. LPP is one of the multi-objective optimization methods and RO is used to make the balance of the preference levels among decision makers. In LPP, the preference ranges of all objective functions are needed, so as the hypothesis of this research. In the research referred in this research, the method to control the effect of RO is not shown. If the effect of RO is too big, the average of the preference level becomes worse. The purpose of this research is to reproduce the mathematical model of multi-player multi-objective decision making based on Suriawase process and propose the method to control the effect of RO. In the proposed model, a set of the solutions of the negotiation problem is obtained and it is proved by the result of the numerical experiment. Therefore, the conclusion that the proposed model is available to obtain a set of the solutions of the negotiation problems in supply chain.

A Multi-Criteria Decision Making for the Unrelated Parallel Machines Scheduling Problem

In this paper, we propose a multi-criteria machine-schedules decision making method that can be applied to a produc-tion environment involving several unrelated parallel machines and we will focus on three objectives: minimizing makespan, total flow time, and total number of tardy jobs. The decision making method consists of three phases. In the first phase, a mathematical model of a single machine scheduling problem, of which the objective is a weighted sum of the three objectives, is constructed. Such a model will be repeatedly solved by the CPLEX in the proposed Multi-Objective Simulated Annealing (MOSA) algorithm. In the second phase, the MOSA that integrates job clustering method, job group scheduling method, and job group – machine assignment method, is employed to obtain a set of non-dominated group schedules. During this phase, CPLEX software and the bipartite weighted matching algorithm are used repeatedly as parts of the MOSA algorithm. In the last phase, the technique of data envelopment analysis is applied to determine the most preferable schedule. A practical example is then presented in order to demonstrate the applicability of the proposed decision making method.

Management of gastroesophageal reflux disease: Patient and physician communication challenges and shared decision making

Gastroesophageal reflux disease(GERD) is a common upper esophageal condition and typical symptoms can include heartburn and sensation of regurgitation while atypical symptoms include chronic cough, asthma, hoarseness, dyspepsia and nausea. Typically, diag-nosis is presumptive given the presence of typical and atypical symptoms and is an indication for empiric therapy. Treatment management can include lifestyle modifications and/or medication therapy with proton pump inhibitor(PPI) class being the preferred and most effective. Complete symptom resolution is not always achieved and long-term PPI therapy can put patients at risk for serious side effects and needless expense. The brain-gut connection and hypervigilance plays an important role in symptom resolution and treatment success, especially in the case of non-PPI responders. Hypervigilance is a combination of increased esophageal sensory sensitivity in combination with exaggerated threat perception surrounding esophageal symptoms. Hypervigilance requires a different approach to GERD managements, where continued PPI therapy and surgery are usually not recommended. Rather, helping physicians and patients understand the brain-gut connection can guide and improve care.Education and reassurance should be the main pillars or treatment. However, it is important not to suggest the symptoms are due to anxiety alone, this often leads to patient dissatisfaction. Patient dissatisfaction with treatment reveals the need for a more patient-centered approach to GERD management and better communication between patients and providers. Shared decision making(SDM) with the incorporation of patient-reported outcomes(PRO) promotes patient adherence and satisfaction. SDM is a joint discussion between clinician and patient in which a mutually shared solution is explored for GERD symptoms. For SDM to work the physician needs to capture patients' perceptions which may not be obtained in the standard interview. This can be done through the use of PROs which promote a dialogue with patients about their symptoms and treatment priorities in the context of the SDM patient encounter. SDM could potentially help in the management of patient expectations for GERD treatment, ultimately positively impacting their health-related quality of life.

Ten misconceptions regarding decision-making in critical care

Diagnostic errors are prevalent in critical care practice and are associated with patient harm and costs for providers and the healthcare system.Patient complexity,illness severity,and the urgency in initiating proper treatment all contribute to decision-making errors.Clinician-related factors such as fatigue,cognitive overload,and inexperience further interfere with effective decision-making.Cognitive science has provided insight into the clinical decision-making process that can be used to reduce error.This evidence-based review discusses ten common misconceptions regarding critical care decision-making.By understanding how practitioners make clinical decisions and examining how errors occur,strategies may be developed and implemented to decrease errors in Decision-making and improve patient outcomes.

A decision support system for satellite layout integrating multi-objective optimization and multi-attribute decision making

A decision support system, including a multi-objective optimization framework and a multi-attribute decision making approach is proposed for satellite equipment layout. Firstly, given three objectives (to minimize the C.G. offset, the cross moments of inertia and the space debris impact risk), we develop a threedimensional layout optimization model. Unlike most of the previous works just focusing on mass characteristics of the system, a space debris impact risk index is developed. Secondly, we develop an efficient optimization framework for the integration of computer-aided design (CAD) software as well as the optimization algorithm to obtain the Pareto front of the layout optimization problem. Thirdly, after obtaining the candidate solutions, we present a multi-attribute decision making approach, which integrates the smart Pareto filter and the correlation coefficient and standard deviation (CCSD) method to select the best tradeoff solutions on the optimal Pareto fronts. Finally, the framework and the decision making approach are applied to a case study of a satellite platform.

Pythagorean Uncertain Linguistic Variable Hamy Mean Operator and Its Application to Multi-attribute Group Decision Making

Pythagorean fuzzy set(PFS) can provide more flexibility than intuitionistic fuzzy set(IFS) for handling uncertain information, and PFS has been increasingly used in multi-attribute decision making problems. This paper proposes a new multiattribute group decision making method based on Pythagorean uncertain linguistic variable Hamy mean(PULVHM) operator and VIKOR method. Firstly, we define operation rules and a new aggregation operator of Pythagorean uncertain linguistic variable(PULV) and explore some properties of the operator.Secondly, taking the decision makers' hesitation degree into account, a new score function is defined, and we further develop a new group decision making approach integrated with VIKOR method. Finally, an investment example is demonstrated to elaborate the validity of the proposed method. Sensibility analysis and comprehensive comparisons with another two methods are performed to show the stability and advantage of our method.

Greedy Algorithm Based Deep Learning Strategy for User Behavior Prediction and Decision Making Support

In this paper, we suggest a deep learning strategy for decision support, based on a greedy algorithm. Decision making support by artificial intelligence is of the most challenging trends in modern computer science. Currently various strategies exist and are increasingly improved in order to meet practical needs of user-oriented platforms like Microsoft, Google, Amazon, etc.

Decision making for pancreatic resection in patients with intraductal papillary mucinous neoplasms

AIM: To identify a practical approach for preoperative decision-making in patients with intraductal papillary mucinous neoplasms (IPMNs) of the pancreas. METHODS: Between March 1999 and November 2006, the clinical characteristics, pathological data and computed tomography/magnetic resonance imaging (CT/MRI) of 54 IPMNs cases were retrieved and analyzed. The relationships between the above data and decision-making for pancreatic resection were analyzed using SPSS 13.0 software. Univariate analysis of risk factors for malignant or invasive IPMNs was performed with regard to the following variables: carcinoembryonic antigen, carbohydrate antigen 19-9 (CA19-9) and the characteristics from CT/MRI images. Receiver operating characteristic (ROC) curve analysis for pancreatic resection was performed using significant factors from the univariate analysis. RESULTS: CT/MRI images, including main and mixed duct IPMNs, tumor size > 30 mm or a solid component appearance in the lesion, and preoperative serum CA19-9 > 37 U/mL had good predictive value for determining pancreatic resection (P < 0.05), but with limitations. Combining the above factors (CT/MRI images and CA19-9) improved the accuracy and sensitivity for determining pancreatic resection in IPMNs. Using ROC analysis, the area under the curve reached 0.893 (P<0.01, 95%CI: 0.763-1.023), with a sensitivity, specificity, positive predictive value and negative predictive value of 95.2%, 83.3%, 95.2% and 83.3%, respectively. CONCLUSION: Combining preoperative CT/MRI images and CA19-9 level may provide useful information for surgical decision-making in IPMNs.

Application of Operational Approaches to Solving Decision Making Problem Using Z-Numbers

The combination of fuzzy logic tools and multi-criteria decision making has a great relevance in literature. Compared with the classical fuzzy number, Z-number has more ability to describe the human knowledge. It can describe both restraint and reliability. Prof. L. Zadeh introduced the concept of Z-numbers to describe the uncertain information which is a more generalized notion closely related to reliability. Use of Z-information is more adequate and intuitively meaningful for formalizing information of a decision making problem. In this paper, Z-number is applied to solve multi-criteria decision making problem. In this paper, we consider two approaches to decision making with Z-information. The first approach is based on converting the Z-numbers to crisp number to determine the priority weight of each alternative. The second approach is based on Expected utility theory by using Z-numbers. To illustrate a validity of suggested approaches to decision making with Z-information the numerical examples have been used.

Lost in the Present: Anterograde Amnesia and Medical Decision Making Capacity

Although there have been a limited number of case reports of human bilateral hippocampal injury, none of these have addressed the impact of such injuries on medical decision making capacity. The authors present a case of an elderly man with discrete bilateral hippocampal injury. As a result of his injury, the patient was hopelessly “lost in the present” and only retained the basic cognitive functions necessary to have decision making capacity for a limited period of time. He was unable to appreciate the nature of his injury, the potential risks involved in his decisions, and the recommended course of treatment longer than a few minutes. The patient’s resultant neurocognitive deficits left him lacking medical decision making capacity, a likely outcome for patients with persistent anterograde amnesia.

A Quantity Model for Controlling and Measuring Software Quality Based on the Expert Decision-Making Algorithm

Researchers have been active in the field of software engineering measurement over more than 30 years. The software quality product is becoming increasingly important in the computerized society. Target setting in software quality function and usability deployment are essential since they are directly related to development of high quality products with high customer satisfaction. Software quality can be measured as the degree to which a particular software program complies with consumer demand regarding function and characteristics. Target setting is usually subjective in practice, which is unscientific. Therefore, this study proposes a quantity model for controlling and measuring software quality via the expert decision-making algorithm-based method for constructing an evaluation method can provide software in relation to users and purchasers, thus enabling administrators or decision makers to identify the most appropriate software quality. Importantly, the proposed model can provide s users and purchasers a reference material, making it highly applicable for academic and government purposes.

An Interactive Expert System Based Decision Making Model for the Management of Transit System Alternate Fuel Vehicle Assets

Traditionally, the process used by public transportation entities to determine the acquisition strategy for new vehicle asset is based upon a broad range of criteria. Vehicle cost has been cited as one of the more critical factors which decision makers consider. It is currently a common practice to consider other factors (life-cycle cost, fuel efficiency, vehicle reliability, environmental effects, etc.) that contribute to a more comprehensive approach. This study investigates the next generation of advancements in decision making tools in the area of the application of methods to quantify and manage uncertainty. In particular, the uncertainty comes from the public policy arena where future policy and regulations are not always based upon logical and predictable processes. The fleet decision making process in most governmental agencies is a very complex and interdependent activity. There are always competing forces and agendas within the view of the decision maker. Rarely is the decision maker a single person although, within the transit environment, there is often one person charged with the responsibility of fleet management. The focus of this research examines the decision making of the general transit agency community via the development of an expert systems prototype tool. A computer-based prototype system is developed which provide an expert knowledge-based recommendation, based upon variable user inputs. The results shown in this study show that a decision making tool for the management of transit system alternate fuel vehicle assets can be modeled and tested. The direct users of this research are the transit agency administrations. The results can be used by the management teams as a reliable input to inform their urban transit buses expansion decision making process.

Neural mechanism of proposer's decision-making in the ultimatum and dictator games

Previous studies have demonstrated that reactions to unfair offers in the ultimatum game are correlated with negative emotion. However, little is known about the difference in neural activity between a proposer＇s decision-making in the ultimatum game compared with the dictator game. The present functional magnetic resonance imaging study revealed that proposing fair offers in the dictator game elicited greater activation in the right supramarginal gyrus, right medial frontal gyrus and left anterior cingulate cortex compared with proposing fair offers in the ultimatum game in 23 Chinese undergraduate and graduate students from Beijing Normal University in China. However, greater activation was found in the right superior temporal gyrus and left cingulate gyrus for the reverse contrast. ＂The results indicate that proposing fair offers in the dictator game is more strongly associated with cognitive control and conflicting information processing compared with proposing fair offers in the ultimatum game.

Driving decision-making analysis of car-following for autonomous vehicle under complex urban environment

The decision-making under complex urban environment become one of the key issues that restricts the rapid development of the autonomous vehicles. The difficulty in making timely and accurate decisions like human beings under highly dynamic traffic environment is a major challenge for autonomous driving. Car-following has been regarded as the simplest but essential driving behavior among driving tasks and has received extensive attention from researchers around the world. This work addresses this problem and proposes a novel method RSAN(rough-set artificial neural network) to learn the decisions from excellent human drivers. A virtual urban traffic environment was built by Pre Scan and driving simulation was conducted to obtain a broad set of relevant data such as experienced drivers' behavior data and surrounding vehicles' motion data. Then, rough set was used to preprocess these data to extract the key influential factors on decision and reduce the impact of uncertain data and noise data. And the car-following decision was learned by neural network in which key factor was the input and acceleration was the output. The result shows the better convergence speed and the better decision accuracy of RSAN than ANN. Findings of this work contributes to the empirical understanding of driver's decision-making process and it provides a theoretical basis for the study of car-following decision-making under complex and dynamic environment.